iii. For problems $1-5,$ you can manipulate the data$-$frames$/$dictionaries as you see fit and using whatever

functions$/$libraries you want. However, it is critically important that your end results for each problem match

the provided variable name $($ex: the result of problem $1$ is called df$\_$prob$1)$ so that they are accessible for

grading.

iv$.$ With the exception of problem $1($which is trivial$)$ you should include a few comments in your notebook or

code that make it clear what your thought process and$/$or code does to address each problem. Failure to do so

will result in penalties $(2$ points$)$ per problem.

Load the $.$csv file into a pandas data$-$frame $($DataFrame$)$ called df$\_$prob$1$ with appropriate rows and columns.

Each row must represent an individual object, while each column must represent a particular attribute$/$variable

$($Object Number, Title, Dynasty, etc.$)$

Many columns of this data are missing entirely $($i$.$e$.$ no values are present for any objects$).$ Use Python to

determine which columns are missing for all of the provided objects and create a modified copy of df$\_$prob$1$ that

excludes these columns entirely called df prob$2.$

Hint: There are multiple ways to do this, but you may want to look into the pandas member function isna.

Suppose we are only interested in objects in the public domain. You are to create a copy of df$\_$prob$2$ called

df$\_$prob$3$ that consists only of those objects where the $"$Is Public Domain" column is "TRUE"; note that if the $"$Is

Public Domain" column does not contain any string for a given object, it should not be present in df$\_$prob$3.$

The objects in the dataset include a column "Artist Role." You are to create a copy of df$\_$prob$3$ called df$\_$prob$4$

that includes the string "Maker" within the "Artist Role" column. Note $-$ while this may seem trivial, the "Artist

Role" column can contain multiple rales for a given object. You must be certain that if at least one of the roles is

Maker, then the object$/$row will appear in the df$\_$prob$4$ data$-$frame.

Hint: you con use regular expressions for this problem, but it is not required, nor is it likely the easiest way to

handle the problem.

Spring $2024$ CSE $532$ Mid$-$term Programming Portion

Nate: for part $5$ you will want to utilize the "Object Begin Date" and "Object End Date" columns in your data

manipulation. The "Object Date" column is unreliable and largely missing in information.

For this part, you are to create a copy of df$\_$prob$4$ called df$\_$prob$5$ and add a new column to df$\_$prob$5$ called

Decodes ToMake, which represents the number of decades that have elapsed between the start year and end

year of the object's creation rounded down. For example, an object with "Object Begin Date" of $1890$ and

"Object End Date" of $1910$ would have a DecadesToMake value of $2,$ while another object with begin date of

$1932$ and end date of $1941$ would have a DecadesToMake value of $0.$ Note: Any object missing either start date

or end date, or with a start date and end date in the same year, should have a DecadesToMake value of $0.$

Most objects in the dataset have a Classification. For this problem, you are to produce a bar plot depicting the

ten most common Classifications for objects in df$\_$probs, with classification labels on the $x-$axis and counts on the

$-$axis, sorted in descending order of counts from left to right. The example below should give you an idea of the

format you want to use, but note that this was generated using the basic Dataframe $($df$\_$prob$1)$ and will not

reflect the expected results you will observe for df$\_$probs. In fact, you may not see some of these same

classifications at all on your plot, for example.

Please code this in a jupyter notebook.